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Found 4 papers, 1 papers with code
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models
A DL model involves a large number of learnable parameters, and its training needs a large dataset of EEG signals, which is difficult to acquire for AER problem.
Eigen Values Features for the Classification of Brain Signals corresponding to 2D and 3D Educational Contents
Eigenvalues features extracted from topomaps images are given to K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers, in order to identify the states of the brain related to incorrect and correct answers.
An Efficient Intelligent System for the Classification of Electroencephalography (EEG) Brain Signals using Nuclear Features for Human Cognitive Tasks
The nuclear features from frontal brain region gave the 100% prediction accuracy.
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach.
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